Overview

Dataset statistics

Number of variables44
Number of observations40336
Missing cells509176
Missing cells (%)28.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Categorical5

Alerts

SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
AST is highly correlated with Bilirubin_directHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with AST and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Unit1 is highly correlated with Unit2High correlation
DBP has 7411 (18.4%) missing values Missing
EtCO2 has 37120 (92.0%) missing values Missing
BaseExcess has 27126 (67.3%) missing values Missing
HCO3 has 20119 (49.9%) missing values Missing
FiO2 has 22527 (55.8%) missing values Missing
pH has 21401 (53.1%) missing values Missing
PaCO2 has 21980 (54.5%) missing values Missing
SaO2 has 27248 (67.6%) missing values Missing
AST has 25979 (64.4%) missing values Missing
BUN has 2018 (5.0%) missing values Missing
Alkalinephos has 26163 (64.9%) missing values Missing
Calcium has 5339 (13.2%) missing values Missing
Chloride has 18925 (46.9%) missing values Missing
Creatinine has 2049 (5.1%) missing values Missing
Bilirubin_direct has 38279 (94.9%) missing values Missing
Glucose has 1580 (3.9%) missing values Missing
Lactate has 27843 (69.0%) missing values Missing
Magnesium has 4931 (12.2%) missing values Missing
Phosphate has 12015 (29.8%) missing values Missing
Potassium has 1867 (4.6%) missing values Missing
Bilirubin_total has 26088 (64.7%) missing values Missing
TroponinI has 33283 (82.5%) missing values Missing
Hct has 2317 (5.7%) missing values Missing
Hgb has 2448 (6.1%) missing values Missing
PTT has 20098 (49.8%) missing values Missing
WBC has 2625 (6.5%) missing values Missing
Fibrinogen has 35821 (88.8%) missing values Missing
Platelets has 2577 (6.4%) missing values Missing
Unit1 has 15617 (38.7%) missing values Missing
Unit2 has 15617 (38.7%) missing values Missing
FiO2 is highly skewed (γ1 = 133.4320486) Skewed
PatientID has unique values Unique
BaseExcess has 3105 (7.7%) zeros Zeros
HospAdmTime has 1313 (3.3%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:26:45.905490
Analysis finished2021-11-29 10:26:58.468624
Duration12.56 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIQUE

Distinct40336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59671.27286
Minimum1
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:58.513716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2017.75
Q110084.75
median20475.5
Q3109916.25
95-th percentile117983.25
Maximum120000
Range119999
Interquartile range (IQR)99831.5

Descriptive statistics

Standard deviation50251.33712
Coefficient of variation (CV)0.842136169
Kurtosis-1.946653503
Mean59671.27286
Median Absolute Deviation (MAD)20307
Skewness0.01560297418
Sum2406900462
Variance2525196883
MonotonicityStrictly increasing
2021-11-29T11:26:58.618229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
1065591
 
< 0.1%
1065521
 
< 0.1%
1065531
 
< 0.1%
1065541
 
< 0.1%
1065551
 
< 0.1%
1065561
 
< 0.1%
1065571
 
< 0.1%
1065581
 
< 0.1%
1065601
 
< 0.1%
Other values (40326)40326
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct276
Distinct (%)0.7%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean102.0500781
Minimum37
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:58.718798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile73
Q189
median100
Q3114
95-th percentile136
Maximum280
Range243
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.25764104
Coefficient of variation (CV)0.1887077541
Kurtosis0.5953510792
Mean102.0500781
Median Absolute Deviation (MAD)12
Skewness0.4855828701
Sum4115781.7
Variance370.8567386
MonotonicityNot monotonic
2021-11-29T11:26:58.816492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901002
 
2.5%
96923
 
2.3%
100916
 
2.3%
88872
 
2.2%
92864
 
2.1%
102845
 
2.1%
94838
 
2.1%
104835
 
2.1%
98820
 
2.0%
106778
 
1.9%
Other values (266)31638
78.4%
ValueCountFrequency (%)
371
 
< 0.1%
421
 
< 0.1%
441
 
< 0.1%
452
 
< 0.1%
484
 
< 0.1%
492
 
< 0.1%
503
 
< 0.1%
514
 
< 0.1%
5210
< 0.1%
534
 
< 0.1%
ValueCountFrequency (%)
2801
< 0.1%
2231
< 0.1%
2111
< 0.1%
2101
< 0.1%
2011
< 0.1%
2001
< 0.1%
1991
< 0.1%
1941
< 0.1%
1931
< 0.1%
1922
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct41
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99.56572747
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:58.909862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile98
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.188173968
Coefficient of variation (CV)0.01193356387
Kurtosis655.579467
Mean99.56572747
Median Absolute Deviation (MAD)0
Skewness-15.52733286
Sum4014291
Variance1.411757378
MonotonicityNot monotonic
2021-11-29T11:26:59.003650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10030838
76.5%
994446
 
11.0%
982554
 
6.3%
971064
 
2.6%
96386
 
1.0%
99.5359
 
0.9%
98.5212
 
0.5%
95136
 
0.3%
97.5112
 
0.3%
96.556
 
0.1%
Other values (31)155
 
0.4%
ValueCountFrequency (%)
271
 
< 0.1%
361
 
< 0.1%
652
< 0.1%
671
 
< 0.1%
713
< 0.1%
741
 
< 0.1%
761
 
< 0.1%
793
< 0.1%
801
 
< 0.1%
822
< 0.1%
ValueCountFrequency (%)
10030838
76.5%
99.5359
 
0.9%
994446
 
11.0%
98.5212
 
0.5%
982554
 
6.3%
97.5112
 
0.3%
971064
 
2.6%
96.556
 
0.1%
96386
 
1.0%
95.521
 
0.1%

Temp
Real number (ℝ≥0)

Distinct277
Distinct (%)0.7%
Missing284
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean37.48079646
Minimum30.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:59.108902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile36.5
Q137
median37.4
Q337.9
95-th percentile38.78
Maximum50
Range19.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7296040907
Coefficient of variation (CV)0.01946607755
Kurtosis5.766888317
Mean37.48079646
Median Absolute Deviation (MAD)0.46
Skewness0.7000706456
Sum1501180.86
Variance0.5323221291
MonotonicityNot monotonic
2021-11-29T11:26:59.208650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
372227
 
5.5%
37.51677
 
4.2%
37.21387
 
3.4%
36.81304
 
3.2%
37.11279
 
3.2%
381265
 
3.1%
37.41209
 
3.0%
37.61208
 
3.0%
37.31125
 
2.8%
36.91125
 
2.8%
Other values (267)26246
65.1%
ValueCountFrequency (%)
30.51
 
< 0.1%
32.62
< 0.1%
32.71
 
< 0.1%
32.81
 
< 0.1%
33.31
 
< 0.1%
33.441
 
< 0.1%
33.53
< 0.1%
33.62
< 0.1%
33.72
< 0.1%
342
< 0.1%
ValueCountFrequency (%)
502
< 0.1%
42.221
< 0.1%
42.11
< 0.1%
41.81
< 0.1%
41.61
< 0.1%
41.51
< 0.1%
41.441
< 0.1%
41.42
< 0.1%
41.31
< 0.1%
41.251
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct473
Distinct (%)1.2%
Missing282
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean152.4966645
Minimum35
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:59.313112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile116
Q1135
median150
Q3168
95-th percentile196.675
Maximum300
Range265
Interquartile range (IQR)33

Descriptive statistics

Standard deviation25.11122854
Coefficient of variation (CV)0.1646673954
Kurtosis0.8941900348
Mean152.4966645
Median Absolute Deviation (MAD)16
Skewness0.5947822324
Sum6108101.4
Variance630.573799
MonotonicityNot monotonic
2021-11-29T11:26:59.418043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140727
 
1.8%
148665
 
1.6%
142646
 
1.6%
146640
 
1.6%
144636
 
1.6%
150630
 
1.6%
136625
 
1.5%
147616
 
1.5%
141612
 
1.5%
149606
 
1.5%
Other values (463)33651
83.4%
ValueCountFrequency (%)
351
 
< 0.1%
662
< 0.1%
69.51
 
< 0.1%
701
 
< 0.1%
722
< 0.1%
742
< 0.1%
753
< 0.1%
75.51
 
< 0.1%
761
 
< 0.1%
771
 
< 0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
2991
 
< 0.1%
2981
 
< 0.1%
2963
< 0.1%
2951
 
< 0.1%
2941
 
< 0.1%
2932
< 0.1%
2922
< 0.1%
2902
< 0.1%
2871
 
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct682
Distinct (%)1.7%
Missing104
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean106.2896853
Minimum22
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:59.520083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile80
Q192
median102.5
Q3116
95-th percentile142
Maximum300
Range278
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.4312629
Coefficient of variation (CV)0.2204471941
Kurtosis14.65292827
Mean106.2896853
Median Absolute Deviation (MAD)11.5
Skewness2.71961821
Sum4276246.62
Variance549.0240809
MonotonicityNot monotonic
2021-11-29T11:26:59.612570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96869
 
2.2%
98856
 
2.1%
100836
 
2.1%
94822
 
2.0%
102805
 
2.0%
97797
 
2.0%
104794
 
2.0%
105767
 
1.9%
95755
 
1.9%
106747
 
1.9%
Other values (672)32184
79.8%
ValueCountFrequency (%)
221
 
< 0.1%
43.51
 
< 0.1%
49.671
 
< 0.1%
501
 
< 0.1%
521
 
< 0.1%
531
 
< 0.1%
542
< 0.1%
54.331
 
< 0.1%
554
< 0.1%
562
< 0.1%
ValueCountFrequency (%)
3005
< 0.1%
2988
< 0.1%
2972
 
< 0.1%
2963
 
< 0.1%
2955
< 0.1%
2947
< 0.1%
2933
 
< 0.1%
2923
 
< 0.1%
2914
< 0.1%
2905
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct376
Distinct (%)1.1%
Missing7411
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean84.28588945
Minimum29
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:59.711426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile60
Q171
median81
Q393.5
95-th percentile117
Maximum300
Range271
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation20.25804646
Coefficient of variation (CV)0.2403492043
Kurtosis16.41241679
Mean84.28588945
Median Absolute Deviation (MAD)11
Skewness2.534419134
Sum2775112.91
Variance410.3884465
MonotonicityNot monotonic
2021-11-29T11:26:59.812670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76833
 
2.1%
80831
 
2.1%
78828
 
2.1%
72808
 
2.0%
82795
 
2.0%
74770
 
1.9%
79754
 
1.9%
77748
 
1.9%
84745
 
1.8%
73723
 
1.8%
Other values (366)25090
62.2%
(Missing)7411
 
18.4%
ValueCountFrequency (%)
291
 
< 0.1%
311
 
< 0.1%
322
< 0.1%
32.751
 
< 0.1%
341
 
< 0.1%
363
< 0.1%
36.51
 
< 0.1%
382
< 0.1%
391
 
< 0.1%
402
< 0.1%
ValueCountFrequency (%)
3002
< 0.1%
2982
< 0.1%
2963
< 0.1%
2932
< 0.1%
2922
< 0.1%
2911
 
< 0.1%
2902
< 0.1%
2872
< 0.1%
2851
 
< 0.1%
2841
 
< 0.1%

Resp
Real number (ℝ≥0)

Distinct177
Distinct (%)0.4%
Missing71
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean26.24052527
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:59.987520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q122
median25
Q329
95-th percentile37
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.601624579
Coefficient of variation (CV)0.2515812664
Kurtosis16.26681439
Mean26.24052527
Median Absolute Deviation (MAD)3
Skewness2.379602644
Sum1056574.75
Variance43.58144708
MonotonicityNot monotonic
2021-11-29T11:27:00.087681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243733
 
9.3%
223373
 
8.4%
252922
 
7.2%
262821
 
7.0%
282628
 
6.5%
202531
 
6.3%
232446
 
6.1%
272076
 
5.1%
301834
 
4.5%
211785
 
4.4%
Other values (167)14116
35.0%
ValueCountFrequency (%)
14
< 0.1%
1.51
 
< 0.1%
29
< 0.1%
36
< 0.1%
45
< 0.1%
4.51
 
< 0.1%
58
< 0.1%
65
< 0.1%
6.51
 
< 0.1%
73
 
< 0.1%
ValueCountFrequency (%)
1005
< 0.1%
997
< 0.1%
986
< 0.1%
975
< 0.1%
96.51
 
< 0.1%
964
< 0.1%
952
 
< 0.1%
943
< 0.1%
932
 
< 0.1%
921
 
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)3.9%
Missing37120
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean38.2994403
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:00.191044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile22
Q133
median38
Q343
95-th percentile52
Maximum100
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.25899176
Coefficient of variation (CV)0.2939727493
Kurtosis9.750683958
Mean38.2994403
Median Absolute Deviation (MAD)5
Skewness1.980621967
Sum123171
Variance126.7648955
MonotonicityNot monotonic
2021-11-29T11:27:00.287555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38149
 
0.4%
41147
 
0.4%
37140
 
0.3%
36139
 
0.3%
42137
 
0.3%
40135
 
0.3%
39135
 
0.3%
35130
 
0.3%
44111
 
0.3%
34106
 
0.3%
Other values (114)1887
 
4.7%
(Missing)37120
92.0%
ValueCountFrequency (%)
104
< 0.1%
10.55
< 0.1%
117
< 0.1%
126
< 0.1%
12.51
 
< 0.1%
134
< 0.1%
13.53
< 0.1%
145
< 0.1%
14.53
< 0.1%
154
< 0.1%
ValueCountFrequency (%)
10010
< 0.1%
994
 
< 0.1%
9810
< 0.1%
979
< 0.1%
963
 
< 0.1%
952
 
< 0.1%
942
 
< 0.1%
932
 
< 0.1%
924
 
< 0.1%
861
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct234
Distinct (%)1.8%
Missing27126
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean1.327452687
Minimum-25
Maximum100
Zeros3105
Zeros (%)7.7%
Negative3065
Negative (%)7.6%
Memory size315.2 KiB
2021-11-29T11:27:00.385954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile-4.555
Q10
median1
Q33
95-th percentile8
Maximum100
Range125
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.168013668
Coefficient of variation (CV)3.139858548
Kurtosis29.91375286
Mean1.327452687
Median Absolute Deviation (MAD)2
Skewness1.548009292
Sum17535.65
Variance17.37233793
MonotonicityNot monotonic
2021-11-29T11:27:00.488726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03105
 
7.7%
11484
 
3.7%
21239
 
3.1%
31102
 
2.7%
-1857
 
2.1%
4848
 
2.1%
-2601
 
1.5%
5567
 
1.4%
6437
 
1.1%
-3415
 
1.0%
Other values (224)2555
 
6.3%
(Missing)27126
67.3%
ValueCountFrequency (%)
-251
 
< 0.1%
-242
 
< 0.1%
-21.81
 
< 0.1%
-212
 
< 0.1%
-201
 
< 0.1%
-196
< 0.1%
-18.51
 
< 0.1%
-18.251
 
< 0.1%
-181
 
< 0.1%
-174
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
49.51
 
< 0.1%
441
 
< 0.1%
361
 
< 0.1%
281
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
247
< 0.1%
232
 
< 0.1%
224
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct182
Distinct (%)0.9%
Missing20119
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean25.58826977
Minimum5
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:00.587786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19.88
Q123
median25
Q328
95-th percentile32
Maximum55
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.998433364
Coefficient of variation (CV)0.1562604037
Kurtosis2.948936497
Mean25.58826977
Median Absolute Deviation (MAD)2
Skewness0.523808741
Sum517318.05
Variance15.98746936
MonotonicityNot monotonic
2021-11-29T11:27:00.685394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252518
 
6.2%
262359
 
5.8%
242205
 
5.5%
272093
 
5.2%
231843
 
4.6%
281570
 
3.9%
221235
 
3.1%
291189
 
2.9%
30783
 
1.9%
21765
 
1.9%
Other values (172)3657
 
9.1%
(Missing)20119
49.9%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
7.71
 
< 0.1%
84
 
< 0.1%
95
 
< 0.1%
109
 
< 0.1%
119
 
< 0.1%
1212
< 0.1%
1323
0.1%
ValueCountFrequency (%)
551
 
< 0.1%
521
 
< 0.1%
503
 
< 0.1%
494
 
< 0.1%
482
 
< 0.1%
474
 
< 0.1%
464
 
< 0.1%
459
< 0.1%
4410
< 0.1%
4313
< 0.1%

FiO2
Real number (ℝ≥0)

MISSING
SKEWED

Distinct87
Distinct (%)0.5%
Missing22527
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean0.9168588916
Minimum0
Maximum4000
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:00.785444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q10.5
median0.62
Q31
95-th percentile1
Maximum4000
Range4000
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation29.96987452
Coefficient of variation (CV)32.68755399
Kurtosis17805.74031
Mean0.9168588916
Median Absolute Deviation (MAD)0.22
Skewness133.4320486
Sum16328.34
Variance898.1933786
MonotonicityNot monotonic
2021-11-29T11:27:00.889031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16549
 
16.2%
0.53591
 
8.9%
0.42233
 
5.5%
0.61106
 
2.7%
0.71087
 
2.7%
0.21620
 
1.5%
0.8536
 
1.3%
0.75343
 
0.9%
0.35281
 
0.7%
0.28233
 
0.6%
Other values (77)1230
 
3.0%
(Missing)22527
55.8%
ValueCountFrequency (%)
02
 
< 0.1%
0.022
 
< 0.1%
0.033
 
< 0.1%
0.046
 
< 0.1%
0.054
 
< 0.1%
0.064
 
< 0.1%
0.081
 
< 0.1%
0.111
 
< 0.1%
0.230
 
0.1%
0.21620
1.5%
ValueCountFrequency (%)
40001
 
< 0.1%
101
 
< 0.1%
71
 
< 0.1%
5.051
 
< 0.1%
214
 
< 0.1%
1.71
 
< 0.1%
1.41
 
< 0.1%
1.31
 
< 0.1%
1.23
 
< 0.1%
16549
16.2%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct82
Distinct (%)0.4%
Missing21401
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean7.42099023
Minimum6.63
Maximum7.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:00.994828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.63
5-th percentile7.31
Q17.38
median7.42
Q37.46
95-th percentile7.52
Maximum7.93
Range1.3
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.06698447184
Coefficient of variation (CV)0.009026352247
Kurtosis5.226422783
Mean7.42099023
Median Absolute Deviation (MAD)0.04
Skewness-0.7051845152
Sum140516.45
Variance0.004486919467
MonotonicityNot monotonic
2021-11-29T11:27:01.100609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.421384
 
3.4%
7.441286
 
3.2%
7.41233
 
3.1%
7.431212
 
3.0%
7.451178
 
2.9%
7.411162
 
2.9%
7.461076
 
2.7%
7.391052
 
2.6%
7.38973
 
2.4%
7.47867
 
2.1%
Other values (72)7512
 
18.6%
(Missing)21401
53.1%
ValueCountFrequency (%)
6.631
 
< 0.1%
6.651
 
< 0.1%
6.811
 
< 0.1%
6.851
 
< 0.1%
6.871
 
< 0.1%
6.941
 
< 0.1%
6.983
< 0.1%
72
< 0.1%
7.011
 
< 0.1%
7.021
 
< 0.1%
ValueCountFrequency (%)
7.931
 
< 0.1%
7.81
 
< 0.1%
7.781
 
< 0.1%
7.731
 
< 0.1%
7.721
 
< 0.1%
7.712
< 0.1%
7.693
< 0.1%
7.682
< 0.1%
7.671
 
< 0.1%
7.663
< 0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct438
Distinct (%)2.4%
Missing21980
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean45.42690673
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:01.207336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile32
Q139
median44
Q350
95-th percentile65
Maximum100
Range90
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.67434797
Coefficient of variation (CV)0.2349785344
Kurtosis4.613701794
Mean45.42690673
Median Absolute Deviation (MAD)5
Skewness1.552377159
Sum833856.3
Variance113.9417045
MonotonicityNot monotonic
2021-11-29T11:27:01.305343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42928
 
2.3%
46891
 
2.2%
44890
 
2.2%
40857
 
2.1%
43845
 
2.1%
45775
 
1.9%
41753
 
1.9%
47739
 
1.8%
48706
 
1.8%
39668
 
1.7%
Other values (428)10304
25.5%
(Missing)21980
54.5%
ValueCountFrequency (%)
101
 
< 0.1%
121
 
< 0.1%
153
< 0.1%
15.31
 
< 0.1%
164
< 0.1%
16.71
 
< 0.1%
173
< 0.1%
184
< 0.1%
18.81
 
< 0.1%
194
< 0.1%
ValueCountFrequency (%)
10016
< 0.1%
9910
< 0.1%
9810
< 0.1%
97.51
 
< 0.1%
9715
< 0.1%
966
 
< 0.1%
95.51
 
< 0.1%
956
 
< 0.1%
94.51
 
< 0.1%
9416
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct239
Distinct (%)1.8%
Missing27248
Missing (%)67.6%
Infinite0
Infinite (%)0.0%
Mean96.38000076
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:01.480393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile85
Q197
median98
Q399
95-th percentile99.6
Maximum100
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.167312673
Coefficient of variation (CV)0.06398954787
Kurtosis21.94861322
Mean96.38000076
Median Absolute Deviation (MAD)1
Skewness-4.365916959
Sum1261421.45
Variance38.0357456
MonotonicityNot monotonic
2021-11-29T11:27:01.576043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
983215
 
8.0%
992103
 
5.2%
971145
 
2.8%
96474
 
1.2%
95217
 
0.5%
99.4203
 
0.5%
99.2196
 
0.5%
99.5187
 
0.5%
99.6180
 
0.4%
100166
 
0.4%
Other values (229)5002
 
12.4%
(Missing)27248
67.6%
ValueCountFrequency (%)
302
 
< 0.1%
341
 
< 0.1%
402
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
461
 
< 0.1%
495
< 0.1%
502
 
< 0.1%
50.31
 
< 0.1%
ValueCountFrequency (%)
100166
0.4%
99.972
 
0.2%
99.851
 
< 0.1%
99.8132
0.3%
99.753
 
< 0.1%
99.7160
0.4%
99.6180
0.4%
99.552
 
< 0.1%
99.5187
0.5%
99.451
 
< 0.1%

AST
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1171
Distinct (%)8.2%
Missing25979
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean182.1310511
Minimum3
Maximum9961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:01.680831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q121
median34
Q374
95-th percentile556.4
Maximum9961
Range9958
Interquartile range (IQR)53

Descriptive statistics

Standard deviation747.9426097
Coefficient of variation (CV)4.106617764
Kurtosis84.87751513
Mean182.1310511
Median Absolute Deviation (MAD)17
Skewness8.618776811
Sum2614855.5
Variance559418.1473
MonotonicityNot monotonic
2021-11-29T11:27:01.783019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19418
 
1.0%
17414
 
1.0%
18410
 
1.0%
21409
 
1.0%
20405
 
1.0%
22386
 
1.0%
16381
 
0.9%
24355
 
0.9%
23347
 
0.9%
15325
 
0.8%
Other values (1161)10507
26.0%
(Missing)25979
64.4%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
53
 
< 0.1%
610
 
< 0.1%
714
 
< 0.1%
824
 
0.1%
950
 
0.1%
9.51
 
< 0.1%
1096
0.2%
11153
0.4%
ValueCountFrequency (%)
99611
< 0.1%
98901
< 0.1%
98401
< 0.1%
97471
< 0.1%
97301
< 0.1%
97101
< 0.1%
96401
< 0.1%
96021
< 0.1%
95821
< 0.1%
95201
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct223
Distinct (%)0.6%
Missing2018
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean24.40460097
Minimum1
Maximum268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:01.882783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q113
median18
Q328
95-th percentile65
Maximum268
Range267
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.13454206
Coefficient of variation (CV)0.8250305785
Kurtosis11.46057572
Mean24.40460097
Median Absolute Deviation (MAD)7
Skewness2.75823894
Sum935135.5
Variance405.3997839
MonotonicityNot monotonic
2021-11-29T11:27:01.984343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141916
 
4.8%
131864
 
4.6%
151853
 
4.6%
121782
 
4.4%
111677
 
4.2%
161674
 
4.2%
171646
 
4.1%
101563
 
3.9%
181438
 
3.6%
191333
 
3.3%
Other values (213)21572
53.5%
(Missing)2018
 
5.0%
ValueCountFrequency (%)
16
 
< 0.1%
229
 
0.1%
3106
 
0.3%
4219
 
0.5%
4.52
 
< 0.1%
5368
0.9%
5.52
 
< 0.1%
6614
1.5%
6.51
 
< 0.1%
7835
2.1%
ValueCountFrequency (%)
2681
< 0.1%
2661
< 0.1%
2521
< 0.1%
2351
< 0.1%
2321
< 0.1%
2271
< 0.1%
2111
< 0.1%
2051
< 0.1%
2021
< 0.1%
2012
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct620
Distinct (%)4.4%
Missing26163
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean100.9139208
Minimum7
Maximum3833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:02.085806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile37
Q155
median74
Q3106
95-th percentile246
Maximum3833
Range3826
Interquartile range (IQR)51

Descriptive statistics

Standard deviation112.1681344
Coefficient of variation (CV)1.111522905
Kurtosis167.928556
Mean100.9139208
Median Absolute Deviation (MAD)23
Skewness9.237644337
Sum1430253
Variance12581.69037
MonotonicityNot monotonic
2021-11-29T11:27:02.190040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55225
 
0.6%
66210
 
0.5%
53209
 
0.5%
58207
 
0.5%
59202
 
0.5%
60202
 
0.5%
69200
 
0.5%
54198
 
0.5%
49196
 
0.5%
61196
 
0.5%
Other values (610)12128
30.1%
(Missing)26163
64.9%
ValueCountFrequency (%)
71
 
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
152
 
< 0.1%
162
 
< 0.1%
171
 
< 0.1%
185
< 0.1%
195
< 0.1%
ValueCountFrequency (%)
38331
< 0.1%
25281
< 0.1%
24401
< 0.1%
21901
< 0.1%
21211
< 0.1%
17991
< 0.1%
16691
< 0.1%
16502
< 0.1%
15461
< 0.1%
15011
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct258
Distinct (%)0.7%
Missing5339
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean8.618354716
Minimum1.07
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:02.298470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile7.5
Q18.1
median8.6
Q39
95-th percentile9.7
Maximum27.9
Range26.83
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.05697304
Coefficient of variation (CV)0.1226420906
Kurtosis47.53124967
Mean8.618354716
Median Absolute Deviation (MAD)0.4
Skewness4.19037443
Sum301616.56
Variance1.117192007
MonotonicityNot monotonic
2021-11-29T11:27:02.398150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.52315
 
5.7%
8.62250
 
5.6%
8.32162
 
5.4%
8.42118
 
5.3%
8.72110
 
5.2%
8.82032
 
5.0%
8.21929
 
4.8%
8.91855
 
4.6%
8.11726
 
4.3%
91691
 
4.2%
Other values (248)14809
36.7%
(Missing)5339
 
13.2%
ValueCountFrequency (%)
1.072
< 0.1%
1.083
< 0.1%
1.091
 
< 0.1%
1.12
< 0.1%
1.112
< 0.1%
1.121
 
< 0.1%
1.132
< 0.1%
1.141
 
< 0.1%
1.161
 
< 0.1%
1.172
< 0.1%
ValueCountFrequency (%)
27.91
< 0.1%
271
< 0.1%
25.21
< 0.1%
24.91
< 0.1%
23.71
< 0.1%
22.61
< 0.1%
22.22
< 0.1%
221
< 0.1%
21.51
< 0.1%
21.21
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct75
Distinct (%)0.4%
Missing18925
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean107.168348
Minimum73
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:02.497402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile98
Q1104
median107
Q3111
95-th percentile116
Maximum145
Range72
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.597582131
Coefficient of variation (CV)0.05223167318
Kurtosis1.843355987
Mean107.168348
Median Absolute Deviation (MAD)3
Skewness0.0271233168
Sum2294581.5
Variance31.33292571
MonotonicityNot monotonic
2021-11-29T11:27:02.595815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081746
 
4.3%
1071726
 
4.3%
1091636
 
4.1%
1061621
 
4.0%
1101502
 
3.7%
1051449
 
3.6%
1041280
 
3.2%
1111256
 
3.1%
1031081
 
2.7%
1121025
 
2.5%
Other values (65)7089
 
17.6%
(Missing)18925
46.9%
ValueCountFrequency (%)
731
 
< 0.1%
742
 
< 0.1%
802
 
< 0.1%
811
 
< 0.1%
822
 
< 0.1%
834
 
< 0.1%
841
 
< 0.1%
853
 
< 0.1%
868
< 0.1%
8711
< 0.1%
ValueCountFrequency (%)
1451
 
< 0.1%
1411
 
< 0.1%
1402
 
< 0.1%
1394
< 0.1%
1376
< 0.1%
1354
< 0.1%
1341
 
< 0.1%
1331
 
< 0.1%
1325
< 0.1%
1318
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1208
Distinct (%)3.2%
Missing2049
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1.559971792
Minimum0.1
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:02.699142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.74
median0.98
Q31.4
95-th percentile5.3
Maximum46.6
Range46.5
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation1.996858622
Coefficient of variation (CV)1.280060724
Kurtosis37.90325903
Mean1.559971792
Median Absolute Deviation (MAD)0.28
Skewness4.94411808
Sum59726.64
Variance3.987444355
MonotonicityNot monotonic
2021-11-29T11:27:02.793875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.82739
 
6.8%
0.72558
 
6.3%
0.92372
 
5.9%
11932
 
4.8%
0.61884
 
4.7%
1.11440
 
3.6%
0.51065
 
2.6%
1.21011
 
2.5%
1.3837
 
2.1%
1.4610
 
1.5%
Other values (1198)21839
54.1%
(Missing)2049
 
5.1%
ValueCountFrequency (%)
0.14
 
< 0.1%
0.221
 
0.1%
0.221
 
< 0.1%
0.241
 
< 0.1%
0.261
 
< 0.1%
0.272
 
< 0.1%
0.281
 
< 0.1%
0.291
 
< 0.1%
0.3117
0.3%
0.315
 
< 0.1%
ValueCountFrequency (%)
46.61
 
< 0.1%
41.91
 
< 0.1%
29.861
 
< 0.1%
29.21
 
< 0.1%
29.11
 
< 0.1%
254
< 0.1%
24.991
 
< 0.1%
24.031
 
< 0.1%
241
 
< 0.1%
23.831
 
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct229
Distinct (%)11.1%
Missing38279
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean1.376305299
Minimum0.01
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:02.963943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.3
Q31
95-th percentile6.2
Maximum37.5
Range37.49
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation3.284154603
Coefficient of variation (CV)2.386210825
Kurtosis34.26925858
Mean1.376305299
Median Absolute Deviation (MAD)0.2
Skewness5.195248659
Sum2831.06
Variance10.78567146
MonotonicityNot monotonic
2021-11-29T11:27:03.062519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1472
 
1.2%
0.2328
 
0.8%
0.3159
 
0.4%
0.4124
 
0.3%
0.564
 
0.2%
0.659
 
0.1%
0.738
 
0.1%
138
 
0.1%
0.835
 
0.1%
1.133
 
0.1%
Other values (219)707
 
1.8%
(Missing)38279
94.9%
ValueCountFrequency (%)
0.015
 
< 0.1%
0.024
 
< 0.1%
0.035
 
< 0.1%
0.044
 
< 0.1%
0.054
 
< 0.1%
0.066
 
< 0.1%
0.077
 
< 0.1%
0.084
 
< 0.1%
0.0910
 
< 0.1%
0.1472
1.2%
ValueCountFrequency (%)
37.51
< 0.1%
351
< 0.1%
302
< 0.1%
29.11
< 0.1%
281
< 0.1%
26.41
< 0.1%
23.621
< 0.1%
22.21
< 0.1%
21.61
< 0.1%
21.21
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct881
Distinct (%)2.3%
Missing1580
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean167.0247082
Minimum19
Maximum988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:03.168299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile93
Q1122
median150
Q3189
95-th percentile299
Maximum988
Range969
Interquartile range (IQR)67

Descriptive statistics

Standard deviation71.84067005
Coefficient of variation (CV)0.4301200154
Kurtosis14.06054082
Mean167.0247082
Median Absolute Deviation (MAD)32
Skewness2.728662747
Sum6473209.59
Variance5161.081873
MonotonicityNot monotonic
2021-11-29T11:27:03.269012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136363
 
0.9%
133359
 
0.9%
146358
 
0.9%
147356
 
0.9%
150351
 
0.9%
142351
 
0.9%
134351
 
0.9%
149349
 
0.9%
129348
 
0.9%
140344
 
0.9%
Other values (871)35226
87.3%
(Missing)1580
 
3.9%
ValueCountFrequency (%)
191
< 0.1%
311
< 0.1%
382
< 0.1%
401
< 0.1%
411
< 0.1%
421
< 0.1%
441
< 0.1%
461
< 0.1%
471
< 0.1%
481
< 0.1%
ValueCountFrequency (%)
9881
< 0.1%
9601
< 0.1%
9521
< 0.1%
9341
< 0.1%
9241
< 0.1%
9141
< 0.1%
9131
< 0.1%
9121
< 0.1%
9071
< 0.1%
8961
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct967
Distinct (%)7.7%
Missing27843
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean2.715240535
Minimum0.3
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:03.370350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.86
Q11.3
median1.9
Q33.1
95-th percentile7.3
Maximum31
Range30.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.507642069
Coefficient of variation (CV)0.9235432505
Kurtosis17.88123839
Mean2.715240535
Median Absolute Deviation (MAD)0.7
Skewness3.506374416
Sum33921.5
Variance6.288268748
MonotonicityNot monotonic
2021-11-29T11:27:03.467630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3415
 
1.0%
1.4404
 
1.0%
1.2394
 
1.0%
1379
 
0.9%
1.6364
 
0.9%
1.5349
 
0.9%
1.1337
 
0.8%
1.7325
 
0.8%
1.8311
 
0.8%
0.9299
 
0.7%
Other values (957)8916
 
22.1%
(Missing)27843
69.0%
ValueCountFrequency (%)
0.32
 
< 0.1%
0.371
 
< 0.1%
0.45
 
< 0.1%
0.522
 
0.1%
0.541
 
< 0.1%
0.552
 
< 0.1%
0.562
 
< 0.1%
0.574
 
< 0.1%
0.591
 
< 0.1%
0.662
0.2%
ValueCountFrequency (%)
311
< 0.1%
28.91
< 0.1%
28.81
< 0.1%
271
< 0.1%
25.91
< 0.1%
24.61
< 0.1%
24.51
< 0.1%
241
< 0.1%
23.51
< 0.1%
23.31
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct81
Distinct (%)0.2%
Missing4931
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2.165394718
Minimum0.5
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:03.570832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.6
Q11.9
median2.1
Q32.3
95-th percentile2.8
Maximum9.8
Range9.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.4169453159
Coefficient of variation (CV)0.1925493363
Kurtosis30.82881911
Mean2.165394718
Median Absolute Deviation (MAD)0.2
Skewness2.977083463
Sum76665.8
Variance0.1738433965
MonotonicityNot monotonic
2021-11-29T11:27:03.665333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.14745
11.8%
24664
11.6%
2.24211
10.4%
1.93569
8.8%
2.33360
8.3%
1.82615
6.5%
2.42499
6.2%
2.51820
 
4.5%
1.71649
 
4.1%
2.61246
 
3.1%
Other values (71)5027
12.5%
(Missing)4931
12.2%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.71
 
< 0.1%
0.81
 
< 0.1%
0.94
 
< 0.1%
113
 
< 0.1%
1.128
 
0.1%
1.258
 
0.1%
1.3138
0.3%
1.4248
0.6%
1.451
 
< 0.1%
ValueCountFrequency (%)
9.81
< 0.1%
9.71
< 0.1%
9.61
< 0.1%
9.31
< 0.1%
8.92
< 0.1%
8.31
< 0.1%
8.21
< 0.1%
8.11
< 0.1%
7.91
< 0.1%
7.62
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct162
Distinct (%)0.6%
Missing12015
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean3.877382508
Minimum0.5
Maximum18.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:03.761668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.2
Q13
median3.6
Q34.4
95-th percentile6.4
Maximum18.8
Range18.3
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.369775641
Coefficient of variation (CV)0.3532732811
Kurtosis7.861905702
Mean3.877382508
Median Absolute Deviation (MAD)0.7
Skewness1.977700817
Sum109811.35
Variance1.876285306
MonotonicityNot monotonic
2021-11-29T11:27:03.857907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.41254
 
3.1%
3.51207
 
3.0%
3.31193
 
3.0%
3.61191
 
3.0%
3.21184
 
2.9%
3.71147
 
2.8%
3.81104
 
2.7%
3.11101
 
2.7%
3.91069
 
2.7%
2.9978
 
2.4%
Other values (152)16893
41.9%
(Missing)12015
29.8%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.63
 
< 0.1%
0.73
 
< 0.1%
0.88
 
< 0.1%
0.94
 
< 0.1%
117
< 0.1%
1.111
 
< 0.1%
1.228
0.1%
1.334
0.1%
1.436
0.1%
ValueCountFrequency (%)
18.81
< 0.1%
17.61
< 0.1%
16.91
< 0.1%
16.51
< 0.1%
16.41
< 0.1%
15.61
< 0.1%
15.51
< 0.1%
14.51
< 0.1%
14.22
< 0.1%
14.11
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct289
Distinct (%)0.8%
Missing1867
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean4.405983519
Minimum2.2
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:03.958310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.5
Q14
median4.3
Q34.7
95-th percentile5.6
Maximum27.5
Range25.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.7254265008
Coefficient of variation (CV)0.164645759
Kurtosis37.05333504
Mean4.405983519
Median Absolute Deviation (MAD)0.4
Skewness2.892950432
Sum169493.78
Variance0.526243608
MonotonicityNot monotonic
2021-11-29T11:27:04.056607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.22973
 
7.4%
4.12943
 
7.3%
42900
 
7.2%
4.32816
 
7.0%
4.42579
 
6.4%
3.92348
 
5.8%
4.52262
 
5.6%
4.61984
 
4.9%
3.81982
 
4.9%
4.71686
 
4.2%
Other values (279)13996
34.7%
(Missing)1867
 
4.6%
ValueCountFrequency (%)
2.21
 
< 0.1%
2.31
 
< 0.1%
2.41
 
< 0.1%
2.54
 
< 0.1%
2.65
 
< 0.1%
2.79
 
< 0.1%
2.818
 
< 0.1%
2.922
 
0.1%
377
0.2%
3.1135
0.3%
ValueCountFrequency (%)
27.51
 
< 0.1%
15.81
 
< 0.1%
131
 
< 0.1%
11.81
 
< 0.1%
11.52
< 0.1%
10.82
< 0.1%
10.751
 
< 0.1%
10.63
< 0.1%
10.42
< 0.1%
10.21
 
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct295
Distinct (%)2.1%
Missing26088
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean1.608660865
Minimum0.1
Maximum49.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:04.154598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.8
Q31.3
95-th percentile5
Maximum49.6
Range49.5
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.393138912
Coefficient of variation (CV)2.109294126
Kurtosis65.09604917
Mean1.608660865
Median Absolute Deviation (MAD)0.3
Skewness7.156927749
Sum22920.2
Variance11.51339168
MonotonicityNot monotonic
2021-11-29T11:27:04.253570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51374
 
3.4%
0.61308
 
3.2%
0.71243
 
3.1%
0.41224
 
3.0%
0.81046
 
2.6%
0.3925
 
2.3%
0.9873
 
2.2%
1717
 
1.8%
1.1566
 
1.4%
1.2475
 
1.2%
Other values (285)4497
 
11.1%
(Missing)26088
64.7%
ValueCountFrequency (%)
0.189
 
0.2%
0.151
 
< 0.1%
0.2472
 
1.2%
0.251
 
< 0.1%
0.3925
2.3%
0.352
 
< 0.1%
0.41224
3.0%
0.451
 
< 0.1%
0.51374
3.4%
0.551
 
< 0.1%
ValueCountFrequency (%)
49.62
< 0.1%
49.21
< 0.1%
46.61
< 0.1%
46.51
< 0.1%
45.91
< 0.1%
45.31
< 0.1%
44.61
< 0.1%
44.31
< 0.1%
44.11
< 0.1%
43.71
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct1281
Distinct (%)18.2%
Missing33283
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean6.930250957
Minimum0.01
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:04.428404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.03
median0.11
Q31.56
95-th percentile40
Maximum440
Range439.99
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation24.90949965
Coefficient of variation (CV)3.594314233
Kurtosis74.54411229
Mean6.930250957
Median Absolute Deviation (MAD)0.1
Skewness7.351661376
Sum48879.06
Variance620.483173
MonotonicityNot monotonic
2021-11-29T11:27:04.523831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011209
 
3.0%
0.03818
 
2.0%
0.02346
 
0.9%
0.04273
 
0.7%
0.05187
 
0.5%
0.06167
 
0.4%
0.07163
 
0.4%
0.08115
 
0.3%
0.1109
 
0.3%
0.0999
 
0.2%
Other values (1271)3567
 
8.8%
(Missing)33283
82.5%
ValueCountFrequency (%)
0.011209
3.0%
0.02346
 
0.9%
0.03818
2.0%
0.04273
 
0.7%
0.05187
 
0.5%
0.06167
 
0.4%
0.07163
 
0.4%
0.08115
 
0.3%
0.0999
 
0.2%
0.1109
 
0.3%
ValueCountFrequency (%)
4402
 
< 0.1%
394.031
 
< 0.1%
381.61
 
< 0.1%
325.311
 
< 0.1%
271.61
 
< 0.1%
243.811
 
< 0.1%
235.881
 
< 0.1%
226.781
 
< 0.1%
20034
0.1%
199.411
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct482
Distinct (%)1.3%
Missing2317
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean33.70793971
Minimum9.3
Maximum71.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:04.621400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile25.5
Q129.9
median33.3
Q337.2
95-th percentile43
Maximum71.7
Range62.4
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.384528664
Coefficient of variation (CV)0.1597406638
Kurtosis0.4701462806
Mean33.70793971
Median Absolute Deviation (MAD)3.6
Skewness0.4199393542
Sum1281542.16
Variance28.99314893
MonotonicityNot monotonic
2021-11-29T11:27:04.719499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32479
 
1.2%
34453
 
1.1%
33425
 
1.1%
35417
 
1.0%
31370
 
0.9%
36360
 
0.9%
30344
 
0.9%
37328
 
0.8%
29323
 
0.8%
38313
 
0.8%
Other values (472)34207
84.8%
(Missing)2317
 
5.7%
ValueCountFrequency (%)
9.31
< 0.1%
111
< 0.1%
12.51
< 0.1%
13.31
< 0.1%
14.41
< 0.1%
14.61
< 0.1%
15.51
< 0.1%
15.81
< 0.1%
161
< 0.1%
16.71
< 0.1%
ValueCountFrequency (%)
71.71
 
< 0.1%
70.21
 
< 0.1%
653
< 0.1%
64.61
 
< 0.1%
621
 
< 0.1%
61.81
 
< 0.1%
61.71
 
< 0.1%
61.21
 
< 0.1%
611
 
< 0.1%
60.51
 
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct244
Distinct (%)0.6%
Missing2448
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean11.2325739
Minimum2.6
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:04.823578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile8.3
Q19.9
median11.1
Q312.4
95-th percentile14.6
Maximum32
Range29.4
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.94150433
Coefficient of variation (CV)0.1728458986
Kurtosis1.573853687
Mean11.2325739
Median Absolute Deviation (MAD)1.3
Skewness0.5845671924
Sum425579.76
Variance3.769439063
MonotonicityNot monotonic
2021-11-29T11:27:04.917612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11825
 
2.0%
10.7821
 
2.0%
11.3821
 
2.0%
10.5814
 
2.0%
10.9811
 
2.0%
11.2800
 
2.0%
10.8797
 
2.0%
10.6775
 
1.9%
10.2768
 
1.9%
10.3763
 
1.9%
Other values (234)29893
74.1%
(Missing)2448
 
6.1%
ValueCountFrequency (%)
2.61
< 0.1%
3.51
< 0.1%
41
< 0.1%
4.31
< 0.1%
4.41
< 0.1%
4.51
< 0.1%
4.81
< 0.1%
4.91
< 0.1%
5.11
< 0.1%
5.41
< 0.1%
ValueCountFrequency (%)
321
 
< 0.1%
301
 
< 0.1%
26.61
 
< 0.1%
251
 
< 0.1%
24.82
< 0.1%
241
 
< 0.1%
23.82
< 0.1%
23.63
< 0.1%
23.41
 
< 0.1%
23.21
 
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct1270
Distinct (%)6.3%
Missing20098
Missing (%)49.8%
Infinite0
Infinite (%)0.0%
Mean43.12384919
Minimum17.1
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:05.014405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile23.6
Q127.8
median32.3
Q342.3
95-th percentile115.23
Maximum250
Range232.9
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation30.99027221
Coefficient of variation (CV)0.7186341849
Kurtosis12.23758064
Mean43.12384919
Median Absolute Deviation (MAD)5.7
Skewness3.202111502
Sum872740.46
Variance960.396972
MonotonicityNot monotonic
2021-11-29T11:27:05.113942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150511
 
1.3%
27.7140
 
0.3%
28.6139
 
0.3%
28.8139
 
0.3%
28.1136
 
0.3%
28.9134
 
0.3%
29.3131
 
0.3%
29.8128
 
0.3%
27.6128
 
0.3%
28.5128
 
0.3%
Other values (1260)18524
45.9%
(Missing)20098
49.8%
ValueCountFrequency (%)
17.11
 
< 0.1%
17.21
 
< 0.1%
17.31
 
< 0.1%
18.11
 
< 0.1%
18.21
 
< 0.1%
18.42
< 0.1%
18.53
< 0.1%
18.61
 
< 0.1%
18.74
< 0.1%
18.83
< 0.1%
ValueCountFrequency (%)
25010
 
< 0.1%
249.913
 
< 0.1%
24956
0.1%
248.71
 
< 0.1%
2481
 
< 0.1%
247.51
 
< 0.1%
2472
 
< 0.1%
246.81
 
< 0.1%
238.11
 
< 0.1%
237.51
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct645
Distinct (%)1.7%
Missing2625
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean12.36481663
Minimum0.1
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:05.214164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5
Q18.2
median11.2
Q314.9
95-th percentile22.9
Maximum440
Range439.9
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation8.174087274
Coefficient of variation (CV)0.6610763036
Kurtosis586.990463
Mean12.36481663
Median Absolute Deviation (MAD)3.3
Skewness15.37822832
Sum466289.6
Variance66.81570276
MonotonicityNot monotonic
2021-11-29T11:27:05.312008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6358
 
0.9%
9.8354
 
0.9%
10353
 
0.9%
8.6352
 
0.9%
10.2350
 
0.9%
10.4348
 
0.9%
11.8341
 
0.8%
8.8341
 
0.8%
9.4336
 
0.8%
8.2335
 
0.8%
Other values (635)34243
84.9%
(Missing)2625
 
6.5%
ValueCountFrequency (%)
0.115
< 0.1%
0.213
< 0.1%
0.39
< 0.1%
0.47
< 0.1%
0.54
 
< 0.1%
0.66
 
< 0.1%
0.74
 
< 0.1%
0.84
 
< 0.1%
0.94
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
4401
< 0.1%
422.91
< 0.1%
3871
< 0.1%
2511
< 0.1%
224.91
< 0.1%
222.81
< 0.1%
215.31
< 0.1%
199.21
< 0.1%
182.61
< 0.1%
170.31
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct718
Distinct (%)15.9%
Missing35821
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean327.1100997
Minimum35
Maximum1760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:05.414096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile138
Q1209.5
median286
Q3399
95-th percentile666
Maximum1760
Range1725
Interquartile range (IQR)189.5

Descriptive statistics

Standard deviation167.059262
Coefficient of variation (CV)0.5107126384
Kurtosis3.412291245
Mean327.1100997
Median Absolute Deviation (MAD)88
Skewness1.48367579
Sum1476902.1
Variance27908.79704
MonotonicityNot monotonic
2021-11-29T11:27:05.510566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21732
 
0.1%
21426
 
0.1%
28025
 
0.1%
21524
 
0.1%
20824
 
0.1%
20024
 
0.1%
18523
 
0.1%
23622
 
0.1%
23322
 
0.1%
20322
 
0.1%
Other values (708)4271
 
10.6%
(Missing)35821
88.8%
ValueCountFrequency (%)
351
 
< 0.1%
52.51
 
< 0.1%
582
< 0.1%
612
< 0.1%
621
 
< 0.1%
631
 
< 0.1%
652
< 0.1%
701
 
< 0.1%
711
 
< 0.1%
763
< 0.1%
ValueCountFrequency (%)
17601
 
< 0.1%
13831
 
< 0.1%
12461
 
< 0.1%
11791
 
< 0.1%
11611
 
< 0.1%
10761
 
< 0.1%
10302
 
< 0.1%
10008
< 0.1%
9941
 
< 0.1%
9791
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct829
Distinct (%)2.2%
Missing2577
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean221.3656612
Minimum4
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:05.612090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile93
Q1154
median204
Q3268
95-th percentile406
Maximum2322
Range2318
Interquartile range (IQR)114

Descriptive statistics

Standard deviation104.945989
Coefficient of variation (CV)0.47408432
Kurtosis13.53349084
Mean221.3656612
Median Absolute Deviation (MAD)56
Skewness2.100815259
Sum8358546
Variance11013.6606
MonotonicityNot monotonic
2021-11-29T11:27:05.712359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167225
 
0.6%
187207
 
0.5%
188205
 
0.5%
202204
 
0.5%
197203
 
0.5%
194202
 
0.5%
175202
 
0.5%
192201
 
0.5%
207201
 
0.5%
186201
 
0.5%
Other values (819)35708
88.5%
(Missing)2577
 
6.4%
ValueCountFrequency (%)
42
< 0.1%
53
< 0.1%
61
 
< 0.1%
72
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
113
< 0.1%
132
< 0.1%
143
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
17831
< 0.1%
16671
< 0.1%
13431
< 0.1%
13391
< 0.1%
12742
< 0.1%
12531
< 0.1%
12011
< 0.1%
11971
< 0.1%
11401
< 0.1%

Age
Real number (ℝ≥0)

Distinct5987
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.64342324
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:05.890456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q151
median63.11
Q374
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.48294561
Coefficient of variation (CV)0.2673917954
Kurtosis-0.2334728394
Mean61.64342324
Median Absolute Deviation (MAD)11.27
Skewness-0.4250999292
Sum2486449.12
Variance271.6874961
MonotonicityNot monotonic
2021-11-29T11:27:05.988483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67581
 
1.4%
68545
 
1.4%
66521
 
1.3%
65512
 
1.3%
61502
 
1.2%
69498
 
1.2%
71490
 
1.2%
62480
 
1.2%
70478
 
1.2%
63473
 
1.2%
Other values (5977)35256
87.4%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
< 0.1%
1832
0.1%
18.113
 
< 0.1%
18.131
 
< 0.1%
18.142
 
< 0.1%
18.151
 
< 0.1%
18.181
 
< 0.1%
ValueCountFrequency (%)
100392
1.0%
89112
 
0.3%
88.991
 
< 0.1%
88.982
 
< 0.1%
88.974
 
< 0.1%
88.961
 
< 0.1%
88.954
 
< 0.1%
88.942
 
< 0.1%
88.931
 
< 0.1%
88.925
 
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
1
22566 
0
17770 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Length

2021-11-29T11:27:06.083325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:27:06.137290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Most occurring characters

ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common40336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII40336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122566
55.9%
017770
44.1%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing15617
Missing (%)38.7%
Memory size315.2 KiB
0.0
12452 
1.0
12267 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.012452
30.9%
1.012267
30.4%
(Missing)15617
38.7%

Length

2021-11-29T11:27:06.190328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:27:06.240486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.012452
50.4%
1.012267
49.6%

Most occurring characters

ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49438
66.7%
Other Punctuation24719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037171
75.2%
112267
 
24.8%
Other Punctuation
ValueCountFrequency (%)
.24719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII74157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing15617
Missing (%)38.7%
Memory size315.2 KiB
1.0
12452 
0.0
12267 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.012452
30.9%
0.012267
30.4%
(Missing)15617
38.7%

Length

2021-11-29T11:27:06.293870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:27:06.344028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.012452
50.4%
0.012267
49.6%

Most occurring characters

ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49438
66.7%
Other Punctuation24719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036986
74.8%
112452
 
25.2%
Other Punctuation
ValueCountFrequency (%)
.24719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII74157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct12156
Distinct (%)30.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-51.84894508
Minimum-5366.86
Maximum23.99
Zeros1313
Zeros (%)3.3%
Negative38767
Negative (%)96.1%
Memory size315.2 KiB
2021-11-29T11:27:06.406197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-240.942
Q1-43.685
median-6.05
Q3-0.04
95-th percentile-0.01
Maximum23.99
Range5390.85
Interquartile range (IQR)43.645

Descriptive statistics

Standard deviation139.766452
Coefficient of variation (CV)-2.695646975
Kurtosis175.3735088
Mean-51.84894508
Median Absolute Deviation (MAD)6.03
Skewness-9.578944604
Sum-2091327.2
Variance19534.6611
MonotonicityNot monotonic
2021-11-29T11:27:06.511105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.023999
 
9.9%
-0.032487
 
6.2%
01313
 
3.3%
-0.011293
 
3.2%
-0.04794
 
2.0%
-0.05436
 
1.1%
-0.06241
 
0.6%
-0.07176
 
0.4%
-0.09108
 
0.3%
-0.0899
 
0.2%
Other values (12146)29389
72.9%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3710.661
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3322.91
< 0.1%
-3269.11
< 0.1%
-3212.561
< 0.1%
-3189.391
< 0.1%
-3141.551
< 0.1%
-3112.121
< 0.1%
ValueCountFrequency (%)
23.991
< 0.1%
22.041
< 0.1%
20.041
< 0.1%
17.341
< 0.1%
16.021
< 0.1%
14.651
< 0.1%
14.211
< 0.1%
141
< 0.1%
11.941
< 0.1%
10.991
< 0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct274
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.01011503
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:06.614784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q124
median39
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.92464081
Coefficient of variation (CV)0.5876588878
Kurtosis43.19490598
Mean39.01011503
Median Absolute Deviation (MAD)11
Skewness4.901225737
Sum1573512
Variance525.5391564
MonotonicityNot monotonic
2021-11-29T11:27:06.715307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361362
 
3.4%
391321
 
3.3%
381318
 
3.3%
411293
 
3.2%
401292
 
3.2%
431239
 
3.1%
421226
 
3.0%
371194
 
3.0%
441141
 
2.8%
461132
 
2.8%
Other values (264)27818
69.0%
ValueCountFrequency (%)
8281
0.7%
9206
 
0.5%
10202
 
0.5%
11207
 
0.5%
12247
 
0.6%
13313
0.8%
14369
0.9%
15506
1.3%
16552
1.4%
17642
1.6%
ValueCountFrequency (%)
33616
< 0.1%
3351
 
< 0.1%
3341
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%

SepsisLabel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
0
37404 
1
 
2932

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Length

2021-11-29T11:27:06.815507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:27:06.869448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring characters

ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common40336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII40336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
0
37404 
1
 
2932

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Length

2021-11-29T11:27:06.925140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:27:06.979179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring characters

ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common40336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII40336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037404
92.7%
12932
 
7.3%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct273
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.48200119
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:27:07.041986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q124
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.79592332
Coefficient of variation (CV)0.5923788425
Kurtosis42.42147962
Mean38.48200119
Median Absolute Deviation (MAD)11
Skewness4.840449261
Sum1552210
Variance519.6541201
MonotonicityNot monotonic
2021-11-29T11:27:07.142479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361364
 
3.4%
391362
 
3.4%
381323
 
3.3%
401285
 
3.2%
411271
 
3.2%
371223
 
3.0%
431210
 
3.0%
421193
 
3.0%
441143
 
2.8%
451089
 
2.7%
Other values (263)27873
69.1%
ValueCountFrequency (%)
8328
0.8%
9236
 
0.6%
10221
 
0.5%
11234
 
0.6%
12285
 
0.7%
13350
0.9%
14412
1.0%
15536
1.3%
16601
1.5%
17721
1.8%
ValueCountFrequency (%)
33610
< 0.1%
3353
 
< 0.1%
3342
 
< 0.1%
3331
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%

Interactions

2021-11-29T11:26:55.088450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:51.550050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:51.648329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:51.737000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:51.832851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:51.932531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.023735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.114123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.205425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.291847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.378008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.469635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.555620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.651845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.743006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.829619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:52.921683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.013319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.172371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.271882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.360544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.456285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.541681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.637579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.726696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.818990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:53.906853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.004081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.090870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.179463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.268994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.361194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.448995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.537607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.622663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.719527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.810267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.898936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:26:54.992652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:27:07.363172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:27:07.706627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:27:08.056494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:27:08.343924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:26:55.404221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:26:56.528436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:26:57.347014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:26:58.201550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
01117.0100.037.44181.0141.33NaN32.0NaN24.048.00.37.40100.091.016.022.098.09.685.00.7NaN193.0NaN2.23.74.60.3NaN37.212.5NaN14.7NaN338.083.140NaNNaN-0.03540054
1294.0100.036.44194.0116.0066.027.0NaNNaN22.0NaNNaNNaNNaNNaN100.0NaN7.9113.02.5NaN78.0NaN2.54.45.1NaNNaN27.89.7NaN11.0NaN158.075.9100.01.0-98.60230023
2393.099.038.61159.099.0069.040.0NaN8.032.00.87.5141.0NaNNaN31.0NaN11.1100.00.9NaN130.0NaN2.52.94.1NaNNaN32.111.030.510.0NaN488.045.8201.00.0-1195.71480048
34113.0100.036.78132.584.0061.526.0NaN0.022.0NaN7.4145.098.0NaN19.0NaN8.2108.00.8NaN253.0NaN2.43.85.0NaNNaN27.68.322.37.6NaN220.065.7100.01.0-8.77290029
4588.099.037.33150.0103.00NaN21.0NaNNaN28.0NaNNaNNaNNaN30.09.080.08.5106.00.7NaN138.0NaN2.53.04.00.6NaN45.715.529.08.1NaN288.028.0911.00.0-0.05490048
56111.0100.036.72150.0100.00NaN43.0NaN0.029.00.47.3447.0NaNNaN9.0NaNNaN111.00.7NaN293.01.4NaNNaN3.8NaNNaN36.912.2NaN12.0NaN298.052.0111.00.0-0.03190017
67155.5100.038.39147.5102.0082.033.0NaN-6.020.01.07.4036.0NaN452.071.088.08.0123.03.9NaN263.02.21.93.84.61.4NaN46.016.427.19.7NaN66.064.2411.00.0-0.05450045
7888.0100.036.89136.081.0056.022.0NaN-6.017.0NaN7.3737.0NaNNaN31.0NaN8.2110.01.3NaN129.02.12.53.85.7NaNNaN32.911.4NaN11.4NaN357.087.081NaNNaN-2.23400040
89143.0100.039.33158.0117.00120.055.5NaN9.035.01.07.5180.599.0NaN25.0NaN8.7113.01.3NaN143.03.82.45.44.5NaNNaN39.514.646.414.9804.0759.027.921NaNNaN-0.0325811258
91084.0100.037.70137.085.0065.023.0NaN0.025.01.07.4243.099.0NaN18.0NaNNaN109.01.1NaN116.01.12.1NaN3.9NaNNaN32.810.929.99.9NaN115.076.7100.01.0-2.36250023

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
4032611999184.0100.037.3172.0112.070.031.540.0NaNNaNNaNNaNNaNNaNNaN14.0NaN7.5NaN0.79NaN155.0NaN1.6NaN4.1NaNNaN26.38.4NaN6.4NaN96.081.000.01.0-66.13250025
40327119992112.0100.037.5213.0153.0111.029.0NaNNaNNaNNaNNaNNaNNaNNaN37.0NaN9.2NaN9.93NaN143.0NaN1.86.64.5NaN0.4130.29.5NaN2.8NaN198.045.011.00.0-4.55410041
4032811999390.598.037.2150.5105.086.020.0NaNNaNNaNNaNNaNNaNNaNNaN15.0NaN8.3NaN1.01NaN132.0NaN2.02.64.1NaN0.0142.014.9NaN12.3NaN175.065.01NaNNaN-3.53210021
4032911999482.0100.037.9146.089.566.026.042.0NaNNaN0.57.440.098.7NaN18.0NaN8.4NaN1.13NaN155.07.212.23.64.8NaNNaN30.310.3NaN11.4NaN71.071.010.01.0-29.57420042
4033011999583.098.036.3175.0124.097.028.0NaNNaNNaNNaNNaNNaNNaNNaN9.0NaN8.8NaN0.81NaN117.0NaN2.03.03.5NaNNaN39.213.1NaN7.0289.0154.076.010.01.0-14.90420042
40331119996124.0100.037.0164.0105.0109.023.0NaNNaNNaNNaNNaNNaNNaN849.08.0259.08.8NaN0.58NaN160.0NaN2.3NaN4.03.30.0142.713.8NaN12.6NaN238.084.00NaNNaN-6.69480048
4033211999780.0100.037.3156.0193.590.026.045.0NaNNaNNaNNaNNaNNaN24.06.0116.017.8NaN0.850.1106.0NaN3.23.13.60.71.0949.416.138.210.8NaN201.030.01NaNNaN-0.02250025
40333119998103.0100.037.6205.5158.5130.526.0NaNNaNNaNNaNNaNNaNNaN9.061.068.08.3NaN8.77NaN91.0NaN1.94.14.30.2NaN30.29.6NaN13.6NaN225.060.001.00.0-53.64490049
40334119999106.0100.037.9164.0114.082.027.0NaNNaNNaNNaNNaNNaNNaN34.029.050.08.5NaN1.04NaN111.0NaNNaNNaN3.41.1NaN26.58.7NaN11.4NaN272.084.001.00.0-10.74200020
40335120000101.099.037.0168.0119.084.020.0NaNNaNNaNNaNNaNNaNNaN18.011.075.09.3NaN0.540.1242.0NaN2.24.03.60.9NaN37.612.029.16.8NaN230.062.00NaNNaN0.00350035